Assessing Similarity Between Ontologies : The Case of the Conceptual Similarity

Bisociative knowledge discovery is an approach that combines elements from two or more ‘incompatible’ domains to generate creative solutions and insight. Inspired by Koestler’s notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network – a graph that captures conceptual similarity between ideas – that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation.

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Effects of perceptual and conceptual similarity in lexical priming of young children who stutter: Preliminary findings

Journal of Fluency Disorders ◽

10.1016/j.jfludis.2006.08.002 ◽

2006 ◽

Vol 31 (4) ◽

pp. 303-324 ◽

Cited By ~ 23

Author(s):

Kia N. Hartfield ◽

Edward G. Conture

Keyword(s):

Young Children ◽

Conceptual Similarity ◽

Lexical Priming

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Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

10.24963/kr.2021/43 ◽

2021 ◽

Author(s):

Ruiqi Li ◽

Hua Hua ◽

Patrik Haslum ◽

Jochen Renz

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Spatial Relations ◽

State Transitions ◽

Conceptual Similarity ◽

Physical Environments ◽

The World ◽

Unsupervised Approach ◽

Action Models ◽

Unseen Objects

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments. We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities. By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions. We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.

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Gradual GRAM and secure computation for RAM programs

Journal of Computer Security ◽

10.3233/jcs-200107 ◽

2021 ◽

pp. 1-33

Author(s):

Carmit Hazay ◽

Mor Lilintal

Keyword(s):

Secure Computation ◽

Boolean Circuits ◽

Conceptual Similarity ◽

Garbled Circuits ◽

Security Guarantees

Despite the fact that the majority of applications encountered in practice today are captured more efficiently by RAM programs, the area of secure two-party computation (2PC) has seen tremendous improvement mostly for Boolean circuits. One of the most studied objects in this domain is garbled circuits. Analogously, garbled RAM (GRAM) provide similar security guarantees for RAM programs with applications to constant round 2PC. In this work we consider the notion of gradual GRAM which requires no memory garbling algorithm. Our approach provides several qualitative advantages over prior works due to the conceptual similarity to the analogue garbling mechanism for Boolean circuits. We next revisit the GRAM construction from (In STOC (2015) 449–458) and improve it in two orthogonal aspects: match it directly with tree-based ORAMs and explore its consistency with gradual ORAM.

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The Blurred Line between Physical Ageing and Mental Health in Older Adults: Implications for the Measurement of Depression

Clinical Medicine Insights Psychiatry ◽

10.1177/1179557319885634 ◽

2019 ◽

Vol 10 ◽

pp. 117955731988563

Author(s):

Cassie M Hazell ◽

Helen E Smith ◽

Christina J Jones

Keyword(s):

Older Adults ◽

Discriminant Validity ◽

Psychological Symptoms ◽

Adult Population ◽

The Elderly ◽

Depression Symptoms ◽

Physical Ageing ◽

Conceptual Similarity ◽

Symptoms Of Depression ◽

General Adult Population

Objectives: Depression in older adults is assessed using measures validated in the general adult population. However, such measures may be inappropriate in the elderly due to the similarities between ageing and the symptoms of depression. This article discusses whether these measures are fit for the purpose and the implications of using inappropriate tools. Methods: A commentary on measuring depression in older adults. Results: Depression symptoms may be mistaken for signs of ageing. Several measures of depression include items that may have a physical cause and thus generate measurement error. Those studies that have assessed the psychometric properties of depression measures in older adults have failed to conduct appropriate assessments of discriminant validity. Discussion: Research is needed to determine whether the conceptual similarity between some symptoms of depression and the effects of ageing translate to factorial similarity. If so, there may be a need for a specific depression measure for older adults that prioritises psychological symptoms.

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